Respiratory muscle ultrasonography evaluation and its clinical application in stroke patients: A review

BackgroundRespiratory muscle ultrasound is a widely available, highly feasible technique that can be used to study the contribution of the individual respiratory muscles related to respiratory dysfunction. Stroke disrupts multiple functions, and the respiratory function is often significantly decreased in stroke patients.MethodA search of the MEDLINE, Web of Science, and PubMed databases was conducted. We identified studies measuring respiratory muscles in healthy and patients by ultrasonography. Two reviewers independently extracted and documented data regarding to the criteria. Data were extracted including participant demographics, ultrasonography evaluation protocol, subject population, reference values, etc.ResultA total of 1954 participants from 39 studies were included. Among them, there were 1,135 participants from 19 studies on diaphragm, 259 participants from 6 studies on extra-diaphragmatic inspiratory muscles, and 560 participants from 14 studies on abdominal expiratory muscles. The ultrasonic evaluation of diaphragm and abdominal expiratory muscle thickness had a relatively typically approach, while, extra-diaphragmatic inspiratory muscles were mainly used in ICU that lack of a consistent paradigm.ConclusionDiaphragm and expiratory muscle ultrasound has been widely used in the assessment of respiratory muscle function. On the contrary, there is not enough evidence to assess extra-diaphragmatic inspiratory muscles by ultrasound. In addition, the thickness of the diaphragm on the hemiplegic side was lower than that on the non-hemiplegic side in stroke patients. For internal oblique muscle (IO), rectus abdominis muscle (RA), transversus abdominis muscle (TrA), and external oblique muscle (EO), most studies showed that the thickness on the hemiplegic side was lower than that on the non-hemiplegic side.Clinical Trial Registration: The protocol of this review was registered in the PROSPERO database (CRD42022352901)

Multispectral Image Enhancement Based on the Dark Channel Prior and Bilateral Fractional Differential Model

Compared with single-band remote sensing images, multispectral images can obtain information on the same target in different bands. By combining the characteristics of each band, we can obtain clearer enhanced images; therefore, we propose a multispectral image enhancement method based on the improved dark channel prior (IDCP) and bilateral fractional differential (BFD) model to make full use of the multiband information. First, the original multispectral image is inverted to meet the prior conditions of dark channel theory. Second, according to the characteristics of multiple bands, the dark channel algorithm is improved. The RGB channels are extended to multiple channels, and the spatial domain fractional differential mask is used to optimize the transmittance estimation to make it more consistent with the dark channel hypothesis. Then, we propose a bilateral fractional differentiation algorithm that enhances the edge details of an image through the fractional differential in the spatial domain and intensity domain. Finally, we implement the inversion operation to obtain the final enhanced image. We apply the proposed IDCP_BFD method to a multispectral dataset and conduct sufficient experiments. The experimental results show the superiority of the proposed method over relative comparison methods

Detail Preserving Low Illumination Image and Video Enhancement Algorithm Based on Dark Channel Prior

In low illumination situations, insufficient light in the monitoring device results in poor visibility of effective information, which cannot meet practical applications. To overcome the above problems, a detail preserving low illumination video image enhancement algorithm based on dark channel prior is proposed in this paper. First, a dark channel refinement method is proposed, which is defined by imposing a structure prior to the initial dark channel to improve the image brightness. Second, an anisotropic guided filter (AnisGF) is used to refine the transmission, which preserves the edges of the image. Finally, a detail enhancement algorithm is proposed to avoid the problem of insufficient detail in the initial enhancement image. To avoid video flicker, the next video frames are enhanced based on the brightness of the first enhanced frame. Qualitative and quantitative analysis shows that the proposed algorithm is superior to the contrast algorithm, in which the proposed algorithm ranks first in average gradient, edge intensity, contrast, and patch-based contrast quality index. It can be effectively applied to the enhancement of surveillance video images and for wider computer vision applications

High-Brightness Image Enhancement Algorithm

In this paper, we introduce a tone mapping algorithm for processing high-brightness video images. This method can maximally recover the information of high-brightness areas and preserve detailed information. Along with benchmark data, real-life and practical application data were taken to test the proposed method. The experimental objects were license plates. We reconstructed the image in the RGB channel, and gamma correction was carried out. After that, local linear adjustment was completed through a tone mapping window to restore the detailed information of the high-brightness region. The experimental results showed that our algorithm could clearly restore the details of high-brightness local areas. The processed image conformed to the visual effect observed by human eyes but with higher definition. Compared with other algorithms, the proposed algorithm has advantages in terms of both subjective and objective evaluation. It can fully satisfy the needs in various practical applications